Evidence map›Paper›PMID 41762245›Full record

ArticleBiological cybernetics2026

A spiking neural network model for fractional proprioceptive encoding of limb posture and movement in insects.

Thomas van der Veen, Yonathan Cohen, Elisabetta Chicca, Volker Dürr

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Article in Biological cybernetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Thomas van der VeenBio-Inspired Circuits and Systems (BICS) Lab, Zernike Institute for Advanced Materials (Zernike Inst Adv Mat), University of Groningen (Univ Groningen), Nijenborgh 3, Groningen, NL-9747 AG, Netherlands.
Yonathan CohenCentre for Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany.
Elisabetta ChiccaBio-Inspired Circuits and Systems (BICS) Lab, Zernike Institute for Advanced Materials (Zernike Inst Adv Mat), University of Groningen (Univ Groningen), Nijenborgh 3, Groningen, NL-9747 AG, Netherlands.
Volker DürrBiological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany. volker.duerr@uni-bielefeld.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proprioception is key to all behaviours that involve the control of force, posture or movement. Computationally, many proprioceptive afferents share three features: First, their strictly local encoding of stimulus magnitudes causes range fractionation in sensory arrays. As a result, encoding of large joint angle ranges requires convergence of afferent information onto first-order interneurons. Second, their phasic-tonic response properties lead to fractional encoding of the fundamental sensory magnitude and its derivatives (e.g., joint angle and angular velocity). Third, the distribution of disjunct sensory arrays across the body implies that complex movements involve information from multiple joints or limbs. The present study proposes a multi-layer spiking neural network for distributed computation of whole-body posture and movement. The first part of the study models strictly local, phasic-tonic encoding of joint angle by proprioceptive hair field afferents by use of Adaptive Exponential Integrate-and-Fire neurons. Fractionally encoded afferent information about single-joint posture and movement converges on two types of first-order interneurons, tuned to encode either joint angle or velocity across the entire working range with high accuracy. In velocity-encoding interneurons, spike rate increases linearly with angular velocity. The companion paper exploits this distributed position/velocity encoding in second- and third-order interneurons, using combinations of two or three position/velocity inputs from disjunct arrays. The encoding properties of all interneuron layers are evaluated with experimental data on whole-body kinematics of unrestrained stick insect locomotion, comprising concurrent joint angle time courses of [Formula: see text] leg joints. The hierarchical model allows increasingly complex encoding of posture and movement, from angular velocity of a single joint, to movement cycle phases of an entire limb, to parameters of overall body posture.

Indexed as

Action PotentialsExtremitiesInsectaModels, NeurologicalNeural Networks, ComputerPostureProprioceptionAnimalsInterneuronsMovementBody postureHair fieldInsect locomotionProprioceptionSensory adaptationSensory encodingSpiking neural network

Identifiers

PMID41762245
PMCPMC12950026

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.